Download Artificial Evolution: 10th International Conference, by Michalis Mavrovouniotis, Shengxiang Yang (auth.), Jin-Kao PDF

By Michalis Mavrovouniotis, Shengxiang Yang (auth.), Jin-Kao Hao, Pierrick Legrand, Pierre Collet, Nicolas Monmarché, Evelyne Lutton, Marc Schoenauer (eds.)

This booklet constitutes chosen most sensible papers from the tenth overseas convention on man made Evolution, EA 2011, held in Angers, France, in October 2011. before everything, 33 complete papers and 10 submit papers have been conscientiously reviewed and chosen from sixty four submissions. This e-book offers the nineteen top papers chosen from those contributions. The papers are equipped in topical sections on ant colony optimization; multi-objective optimization; research; implementation and robotics; combinatorial optimization; studying and parameter tuning; new nature encouraged types; probabilistic algorithms; idea and evolutionary seek; and applications.

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Read or Download Artificial Evolution: 10th International Conference, Evolution Artificielle, EA 2011, Angers, France, October 24-26, 2011, Revised Selected Papers PDF

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Think of σ as a radius at which local optima are searched; however, it is not necessary to assume that ES always finds an optimum x at distance < σ of x, but only that for σ sufficiently small and x sufficiently close to x∗ , x = ES(x, σ, f ) = x∗ . Finally, for any given sequence S = (xi , σi )i∈N ∈ (D×]0, ∞[)N , denote RS(S) the restart algorithm that successively starts from (x1 , σ1 ), (x2 , σ2 ), . . t. X ∗ (f ). x is the output of a whole run of ES: in general, it will be the best point of the run; however, here again the results are more general.

In this work, we use the Nelder-Mead algorithm to optimize the above optimization problem. The solutions xi found will be the new solutions created by the operator N S = {S1 , S2 , S3 }. We recommend to optimize the previous optimization problem with a local search algorithm and use the solution x as initial solution, because in this way if the optimizer can not find an exact solution, at least we can get a solution close to x. Although the error (Equation 6) is a important factor, there is not a strong effect in SIVO, because if we create new solutions with SIVO and they have a big error with respect to the original point, then, in the next generation these solutions created will serve as new solutions for the creation of the surrogate model, and therefore the error would be lower.

Theorem 1 (Consistency of the restart algorithm). Assume that ES has the convergence property (Eq. 1) and the locality property (Eq. 2). Then: 1. t. F ; 2. t. all F ⊂ RD . Proof: (2) is an immediate consequence of (1) so let us prove (1). Let f ∈ F . Eq. 1 immediately implies that {ES(xi , σi , f ); i ≥ 1} ⊂ X ∗ (f ). Let x∗ ∈ X ∗ (f ); then (x∗ , 0) is in Acc S by assumption. Using Eq. 2, it follows that x∗ = ES(xi , σi , f ) for some i ≥ 1; this proves that X ∗ (f ) ⊂ {ES(xi , σi , f ); i ≥ 1}; hence the expected result.

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